{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 Fashion MNIS 数据集\n",
    "### 7.1.1 数据集预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 2us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 2s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# 引入依赖\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 180\n",
    "plt.rcParams['axes.grid'] = False\n",
    "\n",
    "# 加载数据\n",
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress',\n",
    "               'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_images_norm = train_images / 255.0\n",
    "test_images_norm = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x2160 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 12))\n",
    "for i in range(25):\n",
    "    plt.subplot(5, 5, i+1)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 7.2 全连接神经网络\n",
    "### 7.2.1 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "L = keras.layers\n",
    "\n",
    "model = keras.Sequential([\n",
    "    L.Flatten(input_shape=(28, 28)),\n",
    "    L.Dense(128, activation=tf.nn.relu),\n",
    "    L.Dense(10, activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2.2 编译模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=tf.optimizers.Adam(),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 7.2.3 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.5200 - accuracy: 0.8181 - val_loss: 0.4509 - val_accuracy: 0.8352\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3866 - accuracy: 0.8606 - val_loss: 0.3838 - val_accuracy: 0.8591\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3469 - accuracy: 0.8725 - val_loss: 0.3596 - val_accuracy: 0.8676\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3209 - accuracy: 0.8819 - val_loss: 0.3885 - val_accuracy: 0.8612\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.2991 - accuracy: 0.8906 - val_loss: 0.3468 - val_accuracy: 0.8767\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x142855b70>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images_norm,\n",
    "          train_labels,\n",
    "          epochs=5,             # 总共训练 5 轮\n",
    "          validation_split=0.2)  # 使用 20% 数据作为验证数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 7.2.4 评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step - loss: 0.3705 - accuracy: 0.8676\n",
      "Test accuracy: 0.8676000237464905\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images_norm, test_labels)\n",
    "print(f'Test accuracy: {test_acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.9885164e-04 4.2699796e-07 2.9988529e-05 7.8017565e-06 3.4857938e-05\n",
      " 1.2300669e-01 3.2477803e-04 5.7956357e-02 4.0500737e-03 8.1429017e-01]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(test_images_norm)\n",
    "print(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 7.2.5 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.argmax(predictions[0]) # 输出 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2700x2700 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 180\n",
    "plt.rcParams['axes.grid'] = False\n",
    "\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "for index in range(25):\n",
    "    plt.subplot(5, 5, index+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    image = test_images_norm[index]\n",
    "    true_label = test_labels[index]\n",
    "    predict_array = predictions[index]\n",
    "\n",
    "    plt.imshow(image, cmap=plt.cm.binary)\n",
    "\n",
    "    predict_label = np.argmax(predict_array)\n",
    "\n",
    "    if predict_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "    predict_label = f\"{class_names[predict_label]}\"\n",
    "    predict_probility = f\"{100*np.max(predict_array):2.2f}\"\n",
    "    label = f\"{predict_label} {predict_probility}% ({class_names[true_label]})\"\n",
    "    plt.xlabel(label, color=color)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 7.2.6 代码总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.5238 - accuracy: 0.8160 - val_loss: 0.4219 - val_accuracy: 0.8502\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3902 - accuracy: 0.8584 - val_loss: 0.3836 - val_accuracy: 0.8610\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3455 - accuracy: 0.8740 - val_loss: 0.3735 - val_accuracy: 0.8630\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3186 - accuracy: 0.8825 - val_loss: 0.3466 - val_accuracy: 0.8750\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.3029 - accuracy: 0.8865 - val_loss: 0.3369 - val_accuracy: 0.8797\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.3597 - accuracy: 0.8707\n",
      "Test accuracy: 0.8707000017166138\n"
     ]
    }
   ],
   "source": [
    "# 引入依赖\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.python import keras\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 180\n",
    "plt.rcParams['axes.grid'] = False\n",
    "\n",
    "# 加载数据\n",
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress',\n",
    "               'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "# 数据归一化\n",
    "train_images_norm = train_images / 255.0\n",
    "test_images_norm = test_images / 255.0\n",
    "\n",
    "L = keras.layers\n",
    "\n",
    "# 构建模型\n",
    "model = keras.Sequential([\n",
    "    L.Flatten(input_shape=(28, 28)),\n",
    "    L.Dense(128, activation=tf.nn.relu),\n",
    "    L.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "\n",
    "# 编译模型\n",
    "model.compile(optimizer=tf.optimizers.Adam(),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# 训练模型\n",
    "model.fit(train_images_norm,\n",
    "          train_labels,\n",
    "          epochs=5,              # 总共训练 10 轮\n",
    "          validation_split=0.2)   # 使用 20% 数据作为验证数据\n",
    "\n",
    "# 评估模型\n",
    "test_loss, test_acc = model.evaluate(test_images_norm, test_labels)\n",
    "print(f'Test accuracy: {test_acc}')\n",
    "\n",
    "# 预测模型\n",
    "predictions = model.predict(test_images_norm)"
   ]
  },
  {
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   "execution_count": 18,
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    "pycharm": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 256)               409856    \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 431,242\n",
      "Trainable params: 431,242\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 22s 14ms/step - loss: 0.4387 - accuracy: 0.8411 - val_loss: 0.3306 - val_accuracy: 0.8767\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 22s 15ms/step - loss: 0.2941 - accuracy: 0.8924 - val_loss: 0.2960 - val_accuracy: 0.8913\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 24s 16ms/step - loss: 0.2435 - accuracy: 0.9095 - val_loss: 0.2708 - val_accuracy: 0.8996\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 22s 14ms/step - loss: 0.2020 - accuracy: 0.9247 - val_loss: 0.2721 - val_accuracy: 0.9047\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 21s 14ms/step - loss: 0.1644 - accuracy: 0.9389 - val_loss: 0.2795 - val_accuracy: 0.9072\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 0.2844 - accuracy: 0.9016: 0s - loss: 0\n",
      "Conv model test accuracy: 0.9016000032424927\n"
     ]
    }
   ],
   "source": [
    "conv_model = keras.Sequential([\n",
    "    # 卷积层，包括 32 个 5x5 的卷积核，输出维度为 [28-3+1, 28-3+1, 32]\n",
    "    L.Conv2D(input_shape=(28, 28, 1), filters=32, kernel_size=3, strides=1),\n",
    "    # 池化层\n",
    "    L.MaxPool2D(pool_size=2, strides=2),\n",
    "    # 卷积层\n",
    "    L.Conv2D(filters=64, kernel_size=3, strides=1),\n",
    "    # 池化层\n",
    "    L.MaxPool2D(pool_size=2, strides=2),\n",
    "    L.Flatten(),\n",
    "    L.Dense(256, activation=tf.nn.relu),\n",
    "    L.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "\n",
    "conv_model.compile(optimizer=tf.optimizers.Adam(),\n",
    "                   loss='sparse_categorical_crossentropy',\n",
    "                   metrics=['accuracy'])\n",
    "\n",
    "conv_model.summary()\n",
    "\n",
    "# 由于 Conv2D 需要三个维度的张量输入\n",
    "# 把数据从 [28, 28] 即 [x轴色彩通道, y轴色彩通道] 转换成 [x轴, y轴, 色彩通道] 即 [28, 28, 1]\n",
    "train_images_reshape = train_images_norm.reshape([-1, 28, 28, 1])\n",
    "test_images_reshape = test_images_norm.reshape([-1, 28, 28, 1])\n",
    "\n",
    "# 训练模型\n",
    "conv_model.fit(train_images_reshape,\n",
    "               train_labels,\n",
    "               epochs=5,             # 总共训练 10 轮\n",
    "               validation_split=0.2)  # 使用 20% 数据作为验证数据)\n",
    "\n",
    "# 评估模型\n",
    "test_loss, test_acc = conv_model.evaluate(test_images_reshape, test_labels)\n",
    "print(f'Conv model test accuracy: {test_acc}')"
   ]
  },
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   "source": []
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